Machine comprehension (MC) tasks defined in the research community can be categorized along two dimensions. Depending on how the answers are produced, MC tasks can be
(1) extractive, where answer is a text span in the given documents (or passages), such as the SQuAD task (Figure REF  (Left)) [1]}, and
(2) abstractive, where the answer is free-form and needs to be synthesized from the given passages, such as the MS MARCO benchmark (Figure REF  (Left)) [2]}.
Depending on whether conversational context is used, MC tasks can be single-turn, such as SQuAD and MS MARCO, or conversational, such as CoQA and QuAC (Figure REF ).
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